"""simple docstring"""



import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
   from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import MobileNetVaImageProcessor






class  snake_case__      (       snake_case_       ):


 def 						a__   ( self						):
        __a       =    self.config_class(**self.inputs_dict						)
        self.parent.assertTrue(hasattr(lowerCamelCase    ,		"tf_padding"						)						)
        self.parent.assertTrue(hasattr(lowerCamelCase    ,		"depth_multiplier"						)						)






class  snake_case__      :


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase=13    ,		lowerCamelCase=3    ,		lowerCamelCase=32    ,		lowerCamelCase=0.25    ,		lowerCamelCase=8    ,		lowerCamelCase=8    ,		lowerCamelCase=6    ,		lowerCamelCase=32    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase="relu6"    ,		lowerCamelCase=1280    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.02    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=10    ,		lowerCamelCase=None    ,		):
        __a       =    parent
        __a       =    batch_size
        __a       =    num_channels
        __a       =    image_size
        __a       =    depth_multiplier
        __a       =    depth_divisible_by
        __a       =    min_depth
        __a       =    expand_ratio
        __a       =    tf_padding
        __a       =    output_stride
        __a       =    first_layer_is_expansion
        __a       =    finegrained_output
        __a       =    hidden_act
        __a       =    last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier						)
        __a       =    classifier_dropout_prob
        __a       =    use_labels
        __a       =    is_training
        __a       =    num_labels
        __a       =    initializer_range
        __a       =    scope


 def 						a__   ( self						):
        __a       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

        __a       =    None
        __a       =    None
        if self.use_labels:
               __a       =    ids_tensor([self.batch_size]    ,		self.num_labels						)
               __a       =    ids_tensor([self.batch_size, self.image_size, self.image_size]    ,		self.num_labels						)

        __a       =    self.get_config()

        return config, pixel_values, labels, pixel_labels


 def 						a__   ( self						):
        return MobileNetVaConfig(
            num_channels=self.num_channels    ,		image_size=self.image_size    ,		depth_multiplier=self.depth_multiplier    ,		depth_divisible_by=self.depth_divisible_by    ,		min_depth=self.min_depth    ,		expand_ratio=self.expand_ratio    ,		output_stride=self.output_stride    ,		first_layer_is_expansion=self.first_layer_is_expansion    ,		finegrained_output=self.finegrained_output    ,		hidden_act=self.hidden_act    ,		tf_padding=self.tf_padding    ,		classifier_dropout_prob=self.classifier_dropout_prob    ,		initializer_range=self.initializer_range    ,		)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    MobileNetVaModel(config=lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    model(lowerCamelCase						)
        self.parent.assertEqual(
            result.last_hidden_state.shape    ,		(
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )    ,		)
        self.parent.assertEqual(
            result.pooler_output.shape    ,		(self.batch_size, self.last_hidden_size)    ,		)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.num_labels
        __a       =    MobileNetVaForImageClassification(lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    model(lowerCamelCase    ,		labels=lowerCamelCase						)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.num_labels
        __a       =    MobileNetVaForSemanticSegmentation(lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    model(lowerCamelCase						)
        self.parent.assertEqual(
            result.logits.shape    ,		(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )    ,		)
        __a       =    model(lowerCamelCase    ,		labels=lowerCamelCase						)
        self.parent.assertEqual(
            result.logits.shape    ,		(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )    ,		)






 def 						a__   ( self						):
        __a       =    self.prepare_config_and_inputs()
        __a ,   __a ,   __a ,   __a       =    config_and_inputs
        __a       =    {"pixel_values": pixel_values}
        return config, inputs_dict






@require_torch
class  snake_case__      (       snake_case_,						snake_case_,						unittest.TestCase       ):
 _snake_case       :					List[str]							  =						(
     (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 _snake_case       :					Tuple							  =						(
     {
         """feature-extraction""": MobileNetVaModel,
         """image-classification""": MobileNetVaForImageClassification,
         """image-segmentation""": MobileNetVaForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 _snake_case       :					Any							  =						False
 _snake_case       :					List[Any]							  =						False
 _snake_case       :					List[str]							  =						False
 _snake_case       :					Optional[int]							  =						False


 def 						a__   ( self						):
        __a       =    MobileNetVaModelTester(self						)
        __a       =    MobileNetVaConfigTester(self    ,		config_class=lowerCamelCase    ,		has_text_modality=lowerCamelCase						)


 def 						a__   ( self						):
        self.config_tester.run_common_tests()


 @unittest.skip(reason="MobileNetV2 does not use inputs_embeds"						)
 def 						a__   ( self						):
        pass


 @unittest.skip(reason="MobileNetV2 does not support input and output embeddings"						)
 def 						a__   ( self						):
        pass


 @unittest.skip(reason="MobileNetV2 does not output attentions"						)
 def 						a__   ( self						):
        pass


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               __a       =    model_class(lowerCamelCase						)
               __a       =    inspect.signature(model.forward						)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __a       =    [*signature.parameters.keys()]

               __a       =    ["pixel_values"]
               self.assertListEqual(arg_names[:1]    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase						)


 def 						a__   ( self						):
        def check_hidden_states_output(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
               __a       =    model_class(lowerCamelCase						)
               model.to(lowerCamelCase						)
               model.eval()

               with torch.no_grad():
                      __a       =    model(**self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase						)						)

               __a       =    outputs.hidden_states

               __a       =    16
               self.assertEqual(len(lowerCamelCase						)    ,		lowerCamelCase						)

        __a ,   __a       =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               __a       =    True
               check_hidden_states_output(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __a       =    True

               check_hidden_states_output(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase						)






 @slow
 def 						a__   ( self						):
        for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a       =    MobileNetVaModel.from_pretrained(lowerCamelCase						)
               self.assertIsNotNone(lowerCamelCase						)



def 	_lowerCamelCase(	):
       __a       =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
       return image






@require_torch
@require_vision
class  snake_case__      (       unittest.TestCase       ):


 @cached_property
 def 						a__   ( self						):
        return (
            MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224"						) if is_vision_available() else None
        )


 @slow
 def 						a__   ( self						):
        __a       =    MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224"						).to(lowerCamelCase						)

        __a       =    self.default_image_processor
        __a       =    prepare_img()
        __a       =    image_processor(images=lowerCamelCase    ,		return_tensors="pt"						).to(lowerCamelCase						)

        # forward pass
        with torch.no_grad():
               __a       =    model(**lowerCamelCase						)

        # verify the logits
        __a       =    torch.Size((1, 1001)						)
        self.assertEqual(outputs.logits.shape    ,		lowerCamelCase						)

        __a       =    torch.tensor([0.2445, -1.1993, 0.1905]						).to(lowerCamelCase						)

        self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,		lowerCamelCase    ,		atol=1E-4						)						)






 @slow
 def 						a__   ( self						):
        __a       =    MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"						)
        __a       =    model.to(lowerCamelCase						)

        __a       =    MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"						)

        __a       =    prepare_img()
        __a       =    image_processor(images=lowerCamelCase    ,		return_tensors="pt"						).to(lowerCamelCase						)

        # forward pass
        with torch.no_grad():
               __a       =    model(**lowerCamelCase						)
        __a       =    outputs.logits

        # verify the logits
        __a       =    torch.Size((1, 21, 65, 65)						)
        self.assertEqual(logits.shape    ,		lowerCamelCase						)

        __a       =    torch.tensor(
            [
                [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
                [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
            ]    ,		device=lowerCamelCase    ,		)

        self.assertTrue(torch.allclose(logits[0, :3, :3, :3]    ,		lowerCamelCase    ,		atol=1E-4						)						)

"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


SCREAMING_SNAKE_CASE__:Dict									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        warnings.warn(
            "The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
            " use DonutImageProcessor instead."    ,		lowerCamelCase    ,		)
        super().__init__(*lowerCamelCase    ,		**lowerCamelCase						)
